Since the 1950s, many approaches have been proposed for assigning
senses to words in context, although early attempts only served
as models for toy systems. Currently, there are two main
methodological approaches in this area: knowledge-based and
corpus-based methods. Knowledge-based methods use external
knowledge resources, which define explicit sense distinctions
for assigning the correct sense
of a word in context. Corpus-based methods use machine-learning
techniques to induce models of word usages from large collections
of text examples. Both knowledge-based and corpus-based methods
present different benefits and drawbacks.